Automated code refactoring using machine learning
Table Of Contents
<p>1. Introduction<br> 1.1 Significance of automated code refactoring in software development<br> 1.2 Research objectives<br>2. Literature review<br> 2.1 Fundamentals of code refactoring and best practices<br> 2.2 Applications of machine learning in automated software engineering<br> 2.3 Challenges and opportunities in automated code refactoring<br>3. Data collection and feature extraction<br> 3.1 Selection of code repositories and refactoring patterns<br> 3.2 Extraction of code metrics and features<br> 3.3 Ethical considerations and code ownership<br>4. Automated refactoring model development<br> 4.1 Selection of machine learning algorithms for refactoring suggestion generation<br> 4.2 Model training and validation<br> 4.3 Performance evaluation and impact analysis<br>5. Case studies and experiments<br> 5.1 Application of automated refactoring to real-world software projects<br> 5.2 Comparative analysis with manual refactoring efforts<br></p>
Project Abstract
Code refactoring is an essential practice in software development for improving code quality, maintainability, and performance. This project aims to explore the application of machine learning techniques for automated code refactoring, with a focus on identifying refactoring opportunities, generating refactoring suggestions, and evaluating the impact of automated refactoring on software quality. The study will involve analyzing code repositories, feature extraction, model training, and empirical evaluation using real-world software projects. The outcomes of this project will contribute to advancing automated software engineering practices and supporting developers in maintaining high-quality codebases.
Project Overview
Blazingprojects Mobile App
📚 Over 50,000 Project Materials
📱 100% Offline: No internet needed
📝 Over 98 Departments
🔍 Software coding and Machine construction
🎓 Postgraduate/Undergraduate Research works
📥 Instant Whatsapp/Email Delivery
Related Research
Predicting Disease Outbreaks Using Machine Learning and Data Analysis...
The project topic, "Predicting Disease Outbreaks Using Machine Learning and Data Analysis," focuses on utilizing advanced computational techniques to ...
Implementation of a Real-Time Facial Recognition System using Deep Learning Techniqu...
The project on "Implementation of a Real-Time Facial Recognition System using Deep Learning Techniques" aims to develop a sophisticated system that ca...
Applying Machine Learning for Network Intrusion Detection...
The project topic "Applying Machine Learning for Network Intrusion Detection" focuses on utilizing machine learning algorithms to enhance the detectio...
Analyzing and Improving Machine Learning Model Performance Using Explainable AI Tech...
The project topic "Analyzing and Improving Machine Learning Model Performance Using Explainable AI Techniques" focuses on enhancing the effectiveness ...
Applying Machine Learning Algorithms for Predicting Stock Market Trends...
The project topic "Applying Machine Learning Algorithms for Predicting Stock Market Trends" revolves around the application of cutting-edge machine le...
Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems...
The project topic, "Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems," focuses on the integration of machine learn...
Anomaly Detection in Internet of Things (IoT) Networks using Machine Learning Algori...
Anomaly detection in Internet of Things (IoT) networks using machine learning algorithms is a critical research area that aims to enhance the security and effic...
Anomaly Detection in Network Traffic Using Machine Learning Algorithms...
Anomaly detection in network traffic using machine learning algorithms is a crucial aspect of cybersecurity that aims to identify unusual patterns or behaviors ...
Predictive maintenance using machine learning algorithms...
Predictive maintenance is a proactive maintenance strategy that aims to predict equipment failures before they occur, thereby reducing downtime and maintenance ...
